Overview

Dataset statistics

Number of variables14
Number of observations178
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.6 KiB
Average record size in memory112.7 B

Variable types

NUM13
CAT1

Reproduction

Analysis started2020-08-25 02:07:45.292817
Analysis finished2020-08-25 02:08:07.922131
Duration22.63 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Variables

target
Categorical

Distinct count3
Unique (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2
71
1
59
3
48
ValueCountFrequency (%) 
27139.9%
 
15933.1%
 
34827.0%
 
2020-08-25T02:08:07.992131image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
27139.9%
 
15933.1%
 
34827.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number178100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
27139.9%
 
15933.1%
 
34827.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common178100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
27139.9%
 
15933.1%
 
34827.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII178100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
27139.9%
 
15933.1%
 
34827.0%
 

1
Real number (ℝ≥0)

Distinct count126
Unique (%)70.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.00061797752809
Minimum11.03
Maximum14.83
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-08-25T02:08:08.101612image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum11.03
5-th percentile11.6585
Q112.3625
median13.05
Q313.6775
95-th percentile14.2215
Maximum14.83
Range3.8
Interquartile range (IQR)1.315

Descriptive statistics

Standard deviation0.811826538
Coefficient of variation (CV)0.06244522679
Kurtosis-0.8524995685
Mean13.00061798
Median Absolute Deviation (MAD)0.68
Skewness-0.05148233108
Sum2314.11
Variance0.6590623278
2020-08-25T02:08:08.212300image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
12.3763.4%
 
13.0563.4%
 
12.0852.8%
 
12.2942.2%
 
1231.7%
 
12.2531.7%
 
12.4231.7%
 
12.9321.1%
 
12.621.1%
 
12.8521.1%
 
14.121.1%
 
13.1621.1%
 
14.0621.1%
 
13.8821.1%
 
13.5621.1%
 
14.3821.1%
 
13.8621.1%
 
13.1721.1%
 
12.7721.1%
 
13.5821.1%
 
13.4921.1%
 
12.7221.1%
 
12.5121.1%
 
12.721.1%
 
13.7121.1%
 
Other values (101)11262.9%
 
ValueCountFrequency (%) 
11.0310.6%
 
11.4110.6%
 
11.4510.6%
 
11.4610.6%
 
11.5610.6%
 
11.6110.6%
 
11.6210.6%
 
11.6410.6%
 
11.6510.6%
 
11.6610.6%
 
ValueCountFrequency (%) 
14.8310.6%
 
14.7510.6%
 
14.3910.6%
 
14.3821.1%
 
14.3710.6%
 
14.3410.6%
 
14.310.6%
 
14.2310.6%
 
14.2221.1%
 
14.2110.6%
 

2
Real number (ℝ≥0)

Distinct count133
Unique (%)74.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3363483146067416
Minimum0.74
Maximum5.8
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-08-25T02:08:08.335221image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.74
5-th percentile1.061
Q11.6025
median1.865
Q33.0825
95-th percentile4.4555
Maximum5.8
Range5.06
Interquartile range (IQR)1.48

Descriptive statistics

Standard deviation1.117146098
Coefficient of variation (CV)0.478159053
Kurtosis0.2992066799
Mean2.336348315
Median Absolute Deviation (MAD)0.52
Skewness1.039651193
Sum415.87
Variance1.248015403
2020-08-25T02:08:08.436870image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.7373.9%
 
1.8142.2%
 
1.6742.2%
 
1.6831.7%
 
1.6131.7%
 
1.5131.7%
 
1.3531.7%
 
1.5331.7%
 
1.931.7%
 
3.1721.1%
 
3.0321.1%
 
3.4321.1%
 
2.1621.1%
 
2.5921.1%
 
1.6421.1%
 
2.0521.1%
 
1.6521.1%
 
1.1321.1%
 
1.8321.1%
 
1.7121.1%
 
1.8721.1%
 
1.6321.1%
 
1.6621.1%
 
1.7221.1%
 
3.5921.1%
 
Other values (108)11363.5%
 
ValueCountFrequency (%) 
0.7410.6%
 
0.8910.6%
 
0.910.6%
 
0.9210.6%
 
0.9421.1%
 
0.9810.6%
 
0.9910.6%
 
1.0110.6%
 
1.0710.6%
 
1.0910.6%
 
ValueCountFrequency (%) 
5.810.6%
 
5.6510.6%
 
5.5110.6%
 
5.1910.6%
 
5.0410.6%
 
4.9510.6%
 
4.7210.6%
 
4.6110.6%
 
4.610.6%
 
4.4310.6%
 

3
Real number (ℝ≥0)

Distinct count79
Unique (%)44.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3665168539325845
Minimum1.36
Maximum3.23
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-08-25T02:08:08.545452image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.36
5-th percentile1.92
Q12.21
median2.36
Q32.5575
95-th percentile2.7415
Maximum3.23
Range1.87
Interquartile range (IQR)0.3475

Descriptive statistics

Standard deviation0.2743440091
Coefficient of variation (CV)0.1159273422
Kurtosis1.143978169
Mean2.366516854
Median Absolute Deviation (MAD)0.16
Skewness-0.1766993165
Sum421.24
Variance0.07526463531
2020-08-25T02:08:08.649510image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2.373.9%
 
2.2873.9%
 
2.763.4%
 
2.3663.4%
 
2.3263.4%
 
2.4852.8%
 
2.252.8%
 
2.3852.8%
 
2.542.2%
 
2.442.2%
 
2.142.2%
 
2.6242.2%
 
2.2131.7%
 
2.2731.7%
 
2.4531.7%
 
2.1731.7%
 
2.6131.7%
 
2.631.7%
 
1.9231.7%
 
2.3531.7%
 
2.2631.7%
 
2.1231.7%
 
1.9831.7%
 
2.4231.7%
 
2.4631.7%
 
Other values (54)7642.7%
 
ValueCountFrequency (%) 
1.3610.6%
 
1.721.1%
 
1.7110.6%
 
1.7510.6%
 
1.8210.6%
 
1.8810.6%
 
1.910.6%
 
1.9231.7%
 
1.9410.6%
 
1.9510.6%
 
ValueCountFrequency (%) 
3.2310.6%
 
3.2210.6%
 
2.9210.6%
 
2.8710.6%
 
2.8610.6%
 
2.8410.6%
 
2.810.6%
 
2.7810.6%
 
2.7510.6%
 
2.7421.1%
 

4
Real number (ℝ≥0)

Distinct count63
Unique (%)35.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.49494382022472
Minimum10.6
Maximum30.0
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-08-25T02:08:08.768528image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum10.6
5-th percentile14.77
Q117.2
median19.5
Q321.5
95-th percentile25
Maximum30
Range19.4
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation3.339563767
Coefficient of variation (CV)0.171304098
Kurtosis0.4879415405
Mean19.49494382
Median Absolute Deviation (MAD)2.05
Skewness0.2130468864
Sum3470.1
Variance11.15268616
2020-08-25T02:08:08.875955image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
20158.4%
 
21116.2%
 
16116.2%
 
18105.6%
 
1995.1%
 
21.584.5%
 
18.573.9%
 
2273.9%
 
19.573.9%
 
22.573.9%
 
2552.8%
 
16.852.8%
 
2452.8%
 
20.542.2%
 
1731.7%
 
17.231.7%
 
18.831.7%
 
17.531.7%
 
24.531.7%
 
2331.7%
 
28.521.1%
 
18.621.1%
 
15.221.1%
 
15.521.1%
 
1521.1%
 
Other values (38)3921.9%
 
ValueCountFrequency (%) 
10.610.6%
 
11.210.6%
 
11.410.6%
 
1210.6%
 
12.410.6%
 
13.210.6%
 
1421.1%
 
14.610.6%
 
14.810.6%
 
1521.1%
 
ValueCountFrequency (%) 
3010.6%
 
28.521.1%
 
2710.6%
 
26.510.6%
 
2610.6%
 
25.510.6%
 
2552.8%
 
24.531.7%
 
2452.8%
 
23.610.6%
 

5
Real number (ℝ≥0)

Distinct count53
Unique (%)29.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.74157303370787
Minimum70
Maximum162
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-08-25T02:08:08.993963image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile80.85
Q188
median98
Q3107
95-th percentile124.3
Maximum162
Range92
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.28248352
Coefficient of variation (CV)0.1431948894
Kurtosis2.104991324
Mean99.74157303
Median Absolute Deviation (MAD)10
Skewness1.098191055
Sum17754
Variance203.9893354
2020-08-25T02:08:09.084835image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
88137.3%
 
86116.2%
 
10195.1%
 
9895.1%
 
9684.5%
 
10273.9%
 
11263.4%
 
9463.4%
 
8563.4%
 
9752.8%
 
8052.8%
 
9252.8%
 
10352.8%
 
8952.8%
 
9042.2%
 
10842.2%
 
10742.2%
 
10642.2%
 
12031.7%
 
11831.7%
 
11631.7%
 
8731.7%
 
10031.7%
 
8431.7%
 
11131.7%
 
Other values (28)4123.0%
 
ValueCountFrequency (%) 
7010.6%
 
7831.7%
 
8052.8%
 
8110.6%
 
8210.6%
 
8431.7%
 
8563.4%
 
86116.2%
 
8731.7%
 
88137.3%
 
ValueCountFrequency (%) 
16210.6%
 
15110.6%
 
13910.6%
 
13610.6%
 
13410.6%
 
13210.6%
 
12810.6%
 
12710.6%
 
12610.6%
 
12410.6%
 

6
Real number (ℝ≥0)

Distinct count97
Unique (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.295112359550562
Minimum0.98
Maximum3.88
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-08-25T02:08:09.187237image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.98
5-th percentile1.38
Q11.7425
median2.355
Q32.8
95-th percentile3.2745
Maximum3.88
Range2.9
Interquartile range (IQR)1.0575

Descriptive statistics

Standard deviation0.6258510488
Coefficient of variation (CV)0.2726886317
Kurtosis-0.8356265234
Mean2.29511236
Median Absolute Deviation (MAD)0.505
Skewness0.0866385864
Sum408.53
Variance0.3916895353
2020-08-25T02:08:09.287092image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2.284.5%
 
363.4%
 
2.863.4%
 
2.663.4%
 
252.8%
 
2.9552.8%
 
1.3842.2%
 
1.6542.2%
 
2.4542.2%
 
2.8542.2%
 
1.731.7%
 
1.831.7%
 
2.4231.7%
 
1.6831.7%
 
3.331.7%
 
1.4831.7%
 
2.531.7%
 
1.9831.7%
 
2.721.1%
 
2.6521.1%
 
3.1821.1%
 
1.421.1%
 
2.5321.1%
 
1.5521.1%
 
2.5521.1%
 
Other values (72)8849.4%
 
ValueCountFrequency (%) 
0.9810.6%
 
1.110.6%
 
1.1510.6%
 
1.2510.6%
 
1.2810.6%
 
1.310.6%
 
1.3510.6%
 
1.3842.2%
 
1.3921.1%
 
1.421.1%
 
ValueCountFrequency (%) 
3.8810.6%
 
3.8510.6%
 
3.5210.6%
 
3.510.6%
 
3.410.6%
 
3.3810.6%
 
3.331.7%
 
3.2710.6%
 
3.2521.1%
 
3.210.6%
 

7
Real number (ℝ≥0)

Distinct count132
Unique (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0292696629213487
Minimum0.34
Maximum5.08
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-08-25T02:08:09.397723image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.34
5-th percentile0.5455
Q11.205
median2.135
Q32.875
95-th percentile3.4975
Maximum5.08
Range4.74
Interquartile range (IQR)1.67

Descriptive statistics

Standard deviation0.998858685
Coefficient of variation (CV)0.4922257023
Kurtosis-0.8803815472
Mean2.029269663
Median Absolute Deviation (MAD)0.835
Skewness0.02534355338
Sum361.21
Variance0.9977186726
2020-08-25T02:08:09.495473image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2.6542.2%
 
0.5831.7%
 
2.6831.7%
 
0.631.7%
 
1.2531.7%
 
2.0331.7%
 
0.9221.1%
 
0.6621.1%
 
2.4321.1%
 
2.9821.1%
 
0.4721.1%
 
2.2621.1%
 
1.6921.1%
 
2.1721.1%
 
2.7921.1%
 
2.7621.1%
 
2.9221.1%
 
3.1721.1%
 
1.3621.1%
 
3.3921.1%
 
3.1521.1%
 
1.5921.1%
 
1.8421.1%
 
2.6921.1%
 
2.9921.1%
 
Other values (107)12168.0%
 
ValueCountFrequency (%) 
0.3410.6%
 
0.4721.1%
 
0.4810.6%
 
0.4910.6%
 
0.521.1%
 
0.5110.6%
 
0.5210.6%
 
0.5510.6%
 
0.5610.6%
 
0.5710.6%
 
ValueCountFrequency (%) 
5.0810.6%
 
3.9310.6%
 
3.7510.6%
 
3.7410.6%
 
3.6910.6%
 
3.6710.6%
 
3.6410.6%
 
3.5610.6%
 
3.5410.6%
 
3.4910.6%
 

8
Real number (ℝ≥0)

Distinct count39
Unique (%)21.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3618539325842696
Minimum0.13
Maximum0.66
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-08-25T02:08:09.602265image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.13
5-th percentile0.19
Q10.27
median0.34
Q30.4375
95-th percentile0.6
Maximum0.66
Range0.53
Interquartile range (IQR)0.1675

Descriptive statistics

Standard deviation0.1244533403
Coefficient of variation (CV)0.3439325349
Kurtosis-0.6371910641
Mean0.3618539326
Median Absolute Deviation (MAD)0.085
Skewness0.4501513356
Sum64.41
Variance0.01548863391
2020-08-25T02:08:09.703893image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.26116.2%
 
0.43116.2%
 
0.29105.6%
 
0.3295.1%
 
0.384.5%
 
0.3784.5%
 
0.3484.5%
 
0.2784.5%
 
0.484.5%
 
0.2473.9%
 
0.5373.9%
 
0.2163.4%
 
0.2263.4%
 
0.2852.8%
 
0.3952.8%
 
0.1752.8%
 
0.552.8%
 
0.5252.8%
 
0.4742.2%
 
0.4242.2%
 
0.4842.2%
 
0.6342.2%
 
0.5831.7%
 
0.631.7%
 
0.4531.7%
 
Other values (14)2111.8%
 
ValueCountFrequency (%) 
0.1310.6%
 
0.1421.1%
 
0.1752.8%
 
0.1921.1%
 
0.221.1%
 
0.2163.4%
 
0.2263.4%
 
0.2473.9%
 
0.2521.1%
 
0.26116.2%
 
ValueCountFrequency (%) 
0.6610.6%
 
0.6342.2%
 
0.6131.7%
 
0.631.7%
 
0.5831.7%
 
0.5610.6%
 
0.5510.6%
 
0.5373.9%
 
0.5252.8%
 
0.552.8%
 

9
Real number (ℝ≥0)

Distinct count101
Unique (%)56.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5908988764044945
Minimum0.41
Maximum3.58
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-08-25T02:08:09.813830image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.41
5-th percentile0.73
Q11.25
median1.555
Q31.95
95-th percentile2.709
Maximum3.58
Range3.17
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.5723588627
Coefficient of variation (CV)0.3597707379
Kurtosis0.5546485226
Mean1.590898876
Median Absolute Deviation (MAD)0.38
Skewness0.5171371723
Sum283.18
Variance0.3275946677
2020-08-25T02:08:09.914036image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.3595.1%
 
1.4673.9%
 
1.8763.4%
 
1.2552.8%
 
1.5642.2%
 
1.6642.2%
 
1.9842.2%
 
2.0842.2%
 
1.7731.7%
 
1.6331.7%
 
1.9531.7%
 
2.2931.7%
 
1.431.7%
 
2.8131.7%
 
0.8331.7%
 
1.6231.7%
 
2.3831.7%
 
1.9731.7%
 
0.9431.7%
 
1.0331.7%
 
1.1431.7%
 
1.0431.7%
 
1.1521.1%
 
1.4221.1%
 
1.8621.1%
 
Other values (76)8748.9%
 
ValueCountFrequency (%) 
0.4110.6%
 
0.4221.1%
 
0.5510.6%
 
0.6210.6%
 
0.6421.1%
 
0.6810.6%
 
0.7321.1%
 
0.7510.6%
 
0.821.1%
 
0.8110.6%
 
ValueCountFrequency (%) 
3.5810.6%
 
3.2810.6%
 
2.9610.6%
 
2.9121.1%
 
2.8131.7%
 
2.7610.6%
 
2.710.6%
 
2.510.6%
 
2.4910.6%
 
2.4510.6%
 

10
Real number (ℝ≥0)

Distinct count132
Unique (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.058089882022472
Minimum1.28
Maximum13.0
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-08-25T02:08:10.024071image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.28
5-th percentile2.114
Q13.22
median4.69
Q36.2
95-th percentile9.598
Maximum13
Range11.72
Interquartile range (IQR)2.98

Descriptive statistics

Standard deviation2.318285872
Coefficient of variation (CV)0.4583322807
Kurtosis0.3815222728
Mean5.058089882
Median Absolute Deviation (MAD)1.51
Skewness0.868584791
Sum900.339999
Variance5.374449383
2020-08-25T02:08:10.125394image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2.642.2%
 
4.642.2%
 
3.842.2%
 
3.431.7%
 
3.0531.7%
 
2.931.7%
 
531.7%
 
4.531.7%
 
5.731.7%
 
2.831.7%
 
5.631.7%
 
5.431.7%
 
5.131.7%
 
7.321.1%
 
2.0621.1%
 
1.9521.1%
 
4.821.1%
 
7.121.1%
 
6.221.1%
 
2.721.1%
 
7.6521.1%
 
2.4521.1%
 
3.321.1%
 
2.6521.1%
 
4.921.1%
 
Other values (107)11262.9%
 
ValueCountFrequency (%) 
1.2810.6%
 
1.7410.6%
 
1.910.6%
 
1.9521.1%
 
210.6%
 
2.0621.1%
 
2.0810.6%
 
2.1210.6%
 
2.1510.6%
 
2.210.6%
 
ValueCountFrequency (%) 
1310.6%
 
11.7510.6%
 
10.810.6%
 
10.6810.6%
 
10.5210.6%
 
10.2610.6%
 
10.210.6%
 
9.89999910.6%
 
9.710.6%
 
9.5810.6%
 

11
Real number (ℝ≥0)

Distinct count78
Unique (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9574494382022471
Minimum0.48
Maximum1.71
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-08-25T02:08:10.236205image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.48
5-th percentile0.57
Q10.7825
median0.965
Q31.12
95-th percentile1.2845
Maximum1.71
Range1.23
Interquartile range (IQR)0.3375

Descriptive statistics

Standard deviation0.2285715658
Coefficient of variation (CV)0.2387296464
Kurtosis-0.3440957414
Mean0.9574494382
Median Absolute Deviation (MAD)0.165
Skewness0.0210912722
Sum170.426
Variance0.05224496071
2020-08-25T02:08:10.339550image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.0484.5%
 
1.2373.9%
 
1.1263.4%
 
0.8952.8%
 
0.5752.8%
 
0.9652.8%
 
1.2552.8%
 
1.0542.2%
 
1.0942.2%
 
0.7542.2%
 
0.8642.2%
 
0.742.2%
 
1.0742.2%
 
1.1942.2%
 
0.8831.7%
 
0.9531.7%
 
0.9131.7%
 
0.9831.7%
 
1.1331.7%
 
1.0231.7%
 
1.1631.7%
 
0.9331.7%
 
0.631.7%
 
1.0631.7%
 
0.9231.7%
 
Other values (53)7642.7%
 
ValueCountFrequency (%) 
0.4810.6%
 
0.5410.6%
 
0.5510.6%
 
0.5621.1%
 
0.5752.8%
 
0.5821.1%
 
0.5921.1%
 
0.631.7%
 
0.6121.1%
 
0.6210.6%
 
ValueCountFrequency (%) 
1.7110.6%
 
1.4510.6%
 
1.4210.6%
 
1.3810.6%
 
1.3621.1%
 
1.3310.6%
 
1.3121.1%
 
1.2821.1%
 
1.2710.6%
 
1.2552.8%
 

12
Real number (ℝ≥0)

Distinct count122
Unique (%)68.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6116853932584267
Minimum1.27
Maximum4.0
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-08-25T02:08:10.636084image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.27
5-th percentile1.4625
Q11.9375
median2.78
Q33.17
95-th percentile3.58
Maximum4
Range2.73
Interquartile range (IQR)1.2325

Descriptive statistics

Standard deviation0.7099904288
Coefficient of variation (CV)0.2718514376
Kurtosis-1.086434527
Mean2.611685393
Median Absolute Deviation (MAD)0.52
Skewness-0.307285499
Sum464.88
Variance0.5040864089
2020-08-25T02:08:10.740674image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2.8752.8%
 
342.2%
 
1.8242.2%
 
2.7842.2%
 
2.7731.7%
 
1.7531.7%
 
1.3331.7%
 
2.3131.7%
 
3.3331.7%
 
2.9631.7%
 
3.1731.7%
 
1.5631.7%
 
1.5121.1%
 
2.6521.1%
 
3.421.1%
 
2.0621.1%
 
3.2121.1%
 
1.6821.1%
 
2.2621.1%
 
3.321.1%
 
3.5821.1%
 
1.5821.1%
 
3.1621.1%
 
3.2621.1%
 
2.4421.1%
 
Other values (97)11162.4%
 
ValueCountFrequency (%) 
1.2710.6%
 
1.2921.1%
 
1.310.6%
 
1.3331.7%
 
1.3610.6%
 
1.4210.6%
 
1.4710.6%
 
1.4810.6%
 
1.5121.1%
 
1.5510.6%
 
ValueCountFrequency (%) 
410.6%
 
3.9210.6%
 
3.8210.6%
 
3.7110.6%
 
3.6910.6%
 
3.6410.6%
 
3.6310.6%
 
3.5910.6%
 
3.5821.1%
 
3.5710.6%
 

13
Real number (ℝ≥0)

Distinct count121
Unique (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean746.8932584269663
Minimum278
Maximum1680
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-08-25T02:08:10.849710image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum278
5-th percentile354.55
Q1500.5
median673.5
Q3985
95-th percentile1297.25
Maximum1680
Range1402
Interquartile range (IQR)484.5

Descriptive statistics

Standard deviation314.9074743
Coefficient of variation (CV)0.4216231312
Kurtosis-0.2484031061
Mean746.8932584
Median Absolute Deviation (MAD)202.5
Skewness0.7678217814
Sum132947
Variance99166.71736
2020-08-25T02:08:10.956344image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
68052.8%
 
52052.8%
 
63042.2%
 
62542.2%
 
75042.2%
 
51031.7%
 
56231.7%
 
66031.7%
 
45031.7%
 
103531.7%
 
48031.7%
 
49531.7%
 
128531.7%
 
106521.1%
 
104521.1%
 
42821.1%
 
51521.1%
 
41521.1%
 
65021.1%
 
115021.1%
 
38021.1%
 
78021.1%
 
88021.1%
 
56021.1%
 
34521.1%
 
Other values (96)10860.7%
 
ValueCountFrequency (%) 
27810.6%
 
29010.6%
 
31210.6%
 
31510.6%
 
32510.6%
 
34210.6%
 
34521.1%
 
35210.6%
 
35510.6%
 
36510.6%
 
ValueCountFrequency (%) 
168010.6%
 
154710.6%
 
151510.6%
 
151010.6%
 
148010.6%
 
145010.6%
 
137510.6%
 
132010.6%
 
131010.6%
 
129510.6%
 

Interactions

2020-08-25T02:07:45.949521image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:46.093316image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:46.220763image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:46.356055image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:46.497398image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:46.622481image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:46.759613image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:47.075488image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:47.206090image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:47.339355image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:47.465998image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:47.595774image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:47.725893image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:47.862124image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:47.984079image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:48.095755image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:48.216946image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:48.340472image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:48.447986image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:48.563130image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:48.675863image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:48.785725image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:48.906197image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:49.031709image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:49.145344image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:49.255224image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:49.368958image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:49.506016image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:49.629002image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:49.760147image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:49.892683image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:50.011209image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:50.139900image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:50.260406image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:50.388744image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:50.516443image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:50.637797image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:50.758685image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:50.880307image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:51.204122image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:51.356611image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:51.481774image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:51.617832image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:51.766709image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:51.891565image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:52.025329image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:52.153543image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:52.274260image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:52.403761image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:52.528949image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:52.658205image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:52.788167image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:52.923653image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:53.044942image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:53.152813image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:53.266197image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:53.383491image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:53.486957image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:53.598766image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:53.702518image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:53.808158image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:53.920375image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:54.037803image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:54.149988image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:54.261161image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:54.371634image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:54.500854image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:54.617898image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:54.750745image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:54.878331image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:54.988806image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:55.295761image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:55.407224image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:55.518721image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:55.643151image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:55.763603image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:55.880448image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:55.995538image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:56.113827image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:56.236361image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:56.342655image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:56.456602image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:56.576392image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:56.695833image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:56.806493image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:56.912752image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:57.018352image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:57.128782image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:57.235954image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:57.341865image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:57.448631image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:57.564085image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:57.686237image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:57.838209image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:57.960178image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:58.128723image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:58.236054image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:58.349148image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:58.454257image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:58.558565image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:58.671384image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:58.779542image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:58.888001image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:58.995987image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:59.292686image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:59.423951image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:59.540836image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:59.669170image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:59.803252image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:07:59.918268image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:00.038462image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:00.152371image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:00.267793image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:00.387925image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:00.510030image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:00.626512image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:00.747930image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:00.872553image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:00.997989image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:01.110032image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:01.227956image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:01.363745image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:01.473255image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:01.592788image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:01.701635image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:01.816441image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:01.933072image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:02.041991image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:02.156103image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:02.266623image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:02.388560image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:02.510739image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:02.622935image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:02.740660image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:02.865145image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:02.972148image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:03.281745image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:03.391354image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:03.498499image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:03.613366image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:03.723916image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:03.838004image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:03.954517image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:04.075062image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:04.199706image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:04.310567image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:04.432657image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:04.556006image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:04.664305image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:04.782407image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:04.895912image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:05.007348image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:05.126490image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:05.246737image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:05.366014image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:05.499264image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:05.615727image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:05.746949image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:05.867216image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:05.993132image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:06.121377image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:06.232759image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:06.356817image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:06.473529image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:06.586339image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:06.709173image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:06.820668image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:06.938039image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:07.240980image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T02:08:11.097389image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T02:08:11.349540image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T02:08:11.594542image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T02:08:11.829506image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T02:08:07.485178image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:08:07.789962image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

target12345678910111213
0114.231.712.4315.61272.803.060.282.295.641.043.921065
1113.201.782.1411.21002.652.760.261.284.381.053.401050
2113.162.362.6718.61012.803.240.302.815.681.033.171185
3114.371.952.5016.81133.853.490.242.187.800.863.451480
4113.242.592.8721.01182.802.690.391.824.321.042.93735
5114.201.762.4515.21123.273.390.341.976.751.052.851450
6114.391.872.4514.6962.502.520.301.985.251.023.581290
7114.062.152.6117.61212.602.510.311.255.051.063.581295
8114.831.642.1714.0972.802.980.291.985.201.082.851045
9113.861.352.2716.0982.983.150.221.857.221.013.551045

Last rows

target12345678910111213
168313.582.582.6924.51051.550.840.391.548.6600000.741.80750
169313.404.602.8625.01121.980.960.271.118.5000000.671.92630
170312.203.032.3219.0961.250.490.400.735.5000000.661.83510
171312.772.392.2819.5861.390.510.480.649.8999990.571.63470
172314.162.512.4820.0911.680.700.441.249.7000000.621.71660
173313.715.652.4520.5951.680.610.521.067.7000000.641.74740
174313.403.912.4823.01021.800.750.431.417.3000000.701.56750
175313.274.282.2620.01201.590.690.431.3510.2000000.591.56835
176313.172.592.3720.01201.650.680.531.469.3000000.601.62840
177314.134.102.7424.5962.050.760.561.359.2000000.611.60560